scholarly journals Wet snow hazard for power lines: a forecast and alert system applied in Italy

2011 ◽  
Vol 11 (9) ◽  
pp. 2419-2431 ◽  
Author(s):  
P. Bonelli ◽  
M. Lacavalla ◽  
P. Marcacci ◽  
G. Mariani ◽  
G. Stella
Keyword(s):  
Weather Prediction ◽  
Weather Forecast ◽  
Cold Season ◽  
Web Gis ◽  
Power Lines ◽  
Real Problem ◽  
Alert System ◽  
Numerical Weather ◽  
Accretion Model ◽  
Wet Snow

Abstract. Wet snow icing accretion on power lines is a real problem in Italy, causing failures on high and medium voltage power supplies during the cold season. The phenomenon is a process in which many large and local scale variables contribute in a complex way and not completely understood. A numerical weather forecast can be used to select areas where wet snow accretion has an high probability of occurring, but a specific accretion model must also be used to estimate the load of an ice sleeve and its hazard. All the information must be carefully selected and shown to the electric grid operator in order to warn him promptly. The authors describe a prototype of forecast and alert system, WOLF (Wet snow Overload aLert and Forecast), developed and applied in Italy. The prototype elaborates the output of a numerical weather prediction model, as temperature, precipitation, wind intensity and direction, to determine the areas of potential risk for the power lines. Then an accretion model computes the ice sleeves' load for different conductor diameters. The highest values are selected and displayed on a WEB-GIS application principally devoted to the electric operator, but also to more expert users. Some experimental field campaigns have been conducted to better parameterize the accretion model. Comparisons between real accidents and forecasted icing conditions are presented and discussed.

scholarly journals Finite Difference Methods for Weather Prediction in Abuja, Nigeria

2020 ◽  
pp. 915-931
Author(s):  
Jacob Emmanuel ◽  
Ogunfiditimi F.O. ◽  
Victor Alexander Okhuese ◽  
Odeyemi J. K
Keyword(s):  
Finite Difference ◽  
Weather Prediction ◽  
Finite Difference Methods ◽  
Weather Forecast ◽  
Difference Schemes ◽  
Weather Data ◽  
Finite Difference Schemes ◽  
Numerical Weather ◽  
Difference Methods ◽  
The Finite Difference Method

In this research, we have been able to simulate some finite difference schemes to predict weather trends of Abuja Station, Nigeria. By analyzing the results from these schemes, it has shown that the best scheme in the finite difference method that gives a close accurate weather forecast is the trapezoidal scheme hence we use it to simulate numerical weather data obtained from Federal Airports Authority of Nigeria (FAAN), Abuja and corresponding numerical weather data obtained by the compatible finite difference schemes, using MATLAB (R2012a) software to obtain future numerical weather trends.

scholarly journals Prediction of Air Temperature in the Polish Western Carpathian Mountains with the ALADIN-HIRLAM Numerical Weather Prediction System

Atmosphere ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 186 ◽  
Author(s):  
Piotr Sekula ◽  
Anita Bokwa ◽  
Bogdan Bochenek ◽  
Miroslaw Zimnoch
Keyword(s):  
Air Temperature ◽  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Weather Prediction ◽  
Cold Season ◽  
Limited Area ◽  
Spatial And Temporal Variability ◽  
Carpathian Mountains ◽  
Statistical Validation ◽  
Numerical Weather

Prediction of spatial and temporal variability of air temperature in areas with complex topography is still a challenge for numerical weather prediction models. Simulation of atmosphere over complex terrain requires dense and accurate horizontal and vertical grids. In this study, verification results of three configurations of the Aire Limitée Adaptation Dynamique Développement International High-Resolution Limited Area Model (ALADIN-HIRLAM) numerical weather prediction (NWP) system, using two different horizontal and vertical resolutions and applied to the Polish Western Carpathian Mountains, are presented. One model of the ALADIN-HIRLAM NWP system is tested in two horizontal and vertical resolutions. Predicted air temperatures are compared with observations from stations located in different orographies. A comparison of model results with observations was conducted for three cold season intervals in 2017 and 2018. Statistical validation of model output demonstrates better model representativeness for stations located on hill and mountain tops compared to locations in valley bottoms. A comparison of results for two topography representations (2 × 2 km and 1 × 1 km) showed no statistically significant differences of root mean square error (RMSE) and bias between model results and observations.

scholarly journals A model-based approach to adjust microwave observations for operational applications: results of a campaign at Munich Airport in winter 2011/2012

2013 ◽  
Vol 6 (2) ◽  
pp. 2935-2954 ◽  
Author(s):  
J. Güldner
Keyword(s):  
Neural Network ◽  
Weather Prediction ◽  
Microwave Radiometer ◽  
Weather Forecast ◽  
Forecast Model ◽  
Regression Method ◽  
Numerical Weather ◽  
Model Based ◽  
Temperature And Humidity ◽  
Airport Site

Abstract. In the frame of the project "LuFo iPort VIS" which focuses on the implementation of a site specific visibility forecast, a field campaign was organised to offer detailed information to a numerical fog model. As part of additional observing activities a 22-channel microwave radiometer profiler (MWRP) was operating at the Munich Airport site in Germany from October 2011 to February 2012 in order to provide vertical temperature and humidity profiles as well as cloud liquid water information. Independently from the model-related aims of the campaign, the MWRP observations were used to study their capabilities to work in operational meteorological networks. Over the past decade a growing quantity of MWRP has been introduced and a user community (MWRnet) was established to encourage activities directed at the set up of an operational network. On that account, the comparability of observations from different network sites plays a fundamental role for any applications in climatology and numerical weather forecast. In practice, however, systematic temperature and humidity differences (bias) between MWRP retrievals and co-located radiosonde profiles were observed and reported by several authors. This bias can be caused by instrumental offsets as well as by the absorption model used in the retrieval algorithms. At the Lindenberg observatory besides a neural network provided by the manufacturer, a measurement-based regression method was developed to reduce the bias. These regression operators are calculated on the basis of coincident radiosonde observations and MWRP brightness temperature (TB) measurements. However, MWRP applications in a network require comparable results at just any site, even if no radiosondes are available. The motivation of this work is directed to a verification of the suitability of the operational local forecast model COSMO-EU of the Deutscher Wetterdienst (DWD) for the calculation of model-based regression operators in order to provide unbiased vertical profiles during the campaign at Munich Airport. The results of this algorithm and the retrievals of a neural network, specially developed for the site, are compared with radiosondes from Oberschleißheim located about 10 km apart from the MWRP site. The bias of the retrievals could be considerably reduced and the accuracy, which has been assessed for the airport site, is quite similar to those of the operational radiometer site at Lindenberg above 1 km height. Additional investigations are made to determine the length of the training period necessary for generating best estimates. Thereby three months have proven to be adequate. The results of the study show that on the basis of numerical weather prediction (NWP) model data, available everywhere at any time, the model-based regression method is capable to provide comparable results at a multitude of sites. Furthermore, the approach offers auspicious conditions for automation and continuous updating.

scholarly journals Radio Occultation Observations as Anchor Observations in Numerical Weather Prediction Models and Associated Reduction of Bias Corrections in Microwave and Infrared Satellite Observations

2014 ◽  
Vol 31 (1) ◽  
pp. 20-32 ◽  
Author(s):  
L. Cucurull ◽  
R. A. Anthes ◽  
L.-L. Tsao
Keyword(s):  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Radio Occultation ◽  
Prediction Models ◽  
Positive Impact ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Operational Environment ◽  
Numerical Weather ◽  
Bias Corrections

Abstract Satellite radiance measurements are used daily at numerical weather prediction (NWP) centers around the world, providing a significant positive impact on weather forecast skill. Owing to the existence of systematic errors, either in the observations, instruments, and/or forward models, which can be larger than the signal, the use of infrared or microwave radiances in data assimilation systems requires significant bias corrections. As most bias-correction schemes do not correct for biases that exist in the model forecasts, the model needs to be grounded by an unbiased observing system. These reference measurements, also known as “anchor observations,” prevent a drift of the model to its own climatology and associated biases, thus avoiding a spurious drift of the observation bias corrections. This paper shows that the assimilation of global positioning system (GPS) radio occultation (RO) observations over a 3-month period in an operational NWP system results in smaller, more accurate bias corrections in infrared and microwave observations, resulting in an overall more effective use of satellite radiances and a larger number of radiance observations that pass quality control. A full version of the NCEP data assimilation system is used to evaluate the results on the bias corrections for the High Resolution Infrared Radiation Sounder-3 (HIRS-3) on NOAA-17 and the Advanced Microwave Sounding Unit-A (AMSU-A) on NOAA-15 in an operational environment.

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